During any process of adoption of digital analytics systems it becomes inevitable the comparison between 2 solutions that, despite having similar names, differ radically both for their logic and their functionalities. During this post we will point out the main differences to facilitate the decision on which one to use based on the objectives and data strategy for the business.
Both systems have an indicated moment in the analytical maturity of the business for their implementation. We will evaluate 6 components to consider as a digital analytics system in a 3 post installment:
Data exploitation and insights identification
Google Analytics – For its “free” license, as far as data exploitation and insights identification is concerned it is quite limited. You have to squeeze the knowledge of analysts in a tool that can not deliver differential value to the business.
If we talk about the paid license, it is possible to integrate data extraction, transformation and loading processes in Google’s own systems such as GCP or with data processors such as Python and R. This sometimes represents an added value for clients, but it also hinders and slows down a process that should be agile and in line with the immediacy of the medium (analysis/diagnosis/conclusions/recommendations). Recently Google has launched in beta versions with modules that use AI/ML suggesting digital insights, however, in this sense it is in its first steps. Google Analytics 360 has a maximum capacity to create 50 metrics calculated using basic mathematical operations.
Adobe Analytics – Unlike Google, Adobe has different packages or tiers for Analytics, each one catering to the analytical maturity and the need and context of each business. The main difference is that Adobe extends the capability of its AI/ML engine (Adobe Sensei) and offers quite telling functionalities with its title alone: Anomaly Detection, Contribution Analysis, Segment Comparison and Algorithmic Attribution Models. Similar to Google, Adobe also allows you to use data processors to manipulate and create models in house. Adobe Analytics has the ability to create calculated metrics without a defined limit and uses complex mathematical, statistical and sequential operations on demand.
As a conclusion, Adobe Analytics is in my opinion much more robust and powerful than Google Analytics in data exploitation and insights identification. Choosing one will depend on the current state of the data strategy (defensive or offensive) and its analytical maturity (team structure, process execution, roles, decision making, data democratization, data ethics and regulations and governance).
As a bonus, both solutions have native APIs in data visualization tools such as Power BI, Datorama, Tableau and QlikView. It goes without saying that Adobe Analytics has its own built-in and configured visualization tool “Workspace” where the mentioned functionalities run and has a drag and drop UI, where Google offers a rigid UI and an alternative that requires integration and configuration called Data Studio.
Data Collection
Google Analytics – Returning to the “free” license (since it is more used than its paid license), they have an important limitation to collect data and that is the data sampling. With this license it is impossible to see the global behavior of the customers since it works with a representative sampling, which makes it an unreliable source of data. This improves in its paid license, however, when the threshold of hits allowed monthly is exceeded or custom requests are made, there is again a sampling of the information.
Adobe Analytics – Adobe has the full view of digital behavior without sampling, regardless of the number of hits/requests or custom reports requested.
To conclude, on a technical level both use librearia js (with multiple names) to collect data, however, there are metrics that have different logics and measurement method. As you could see, each solution is quite different in terms of capabilities and functionalities. I have had the pleasure of working with both and in my opinion Adobe represents a competitive advantage in the marketplace primarily because of its ability to deliver value in a relatively short time.”
Author: Philipe Castillo
November 18, 2021